tl;dr

This notebook determines the model framework utilized for this PRD. It utilizes the results from the hyperparameter tuning step, and trains the optimal model for each algorithm on the v67 dataset. The resultant predictions of these optimal models are compared to a v68 validation dataset. The framework used to generate the highest performant model (e.g., features, hyperparameters) will be used in online model training for the subsetting of Beta in production use.

Features

Feature sets:

  1. Only performance metrics.
  2. Performance metrics and the highest found by Boruta with original dataset.
  3. Performance metrics and the highest found by Boruta with equal labels dataset.
  4. Performance metrics and all covariates.
  5. Only the highest covariates found by Boruta (excluding performance).
  6. Only the highest covariates found by Boruta (equal labels, excluding performance).
  7. Utilize all covariates (excluding performance).

Validation Bootstraps

30 bootstrap replicates of v68 Release will be utilized to validate the models. There are 257697 profiles utilized for this purpose.

[1] "Loading previously created bootstraps from file"

Oversampling

The entire v67 dataset will be utilized for training. However, most of the matching algorithms are more performant with higher ratios of Beta to Release. Therefore, various levels of oversampled datasets are created for training. There are 70345 profiles utilized for this purpose.

[1] "Loading previously created oversampling training sets from file"

Model Training

Two matching methods were ultimately tested, as CEM and subclassing were producing extremely poor results.

  • nearest-neighbors
  • genetic matching

For all models the following diagnostics are reported:

  • mean, median score against the 30 validation replicates
  • Number of matched Beta samples
  • QQ and ridge plots of original and matched Beta samples

Nearest-Neighbors, Malahanobis

Hyperparameters: hyperparameter_tuning_nn_malahanobis.Rmd

Feature selection and hyperparameter tuning for the nearest-neighbors models were all trained using the MatchIt library. These models are trained using the full, relevantly oversampled, training dataset.

FS 1

  • caliper = 0.20
  • calcloset = TRUE
  • ratio = 2
  • replace = FALSE
  • 4x oversampling

Resultant scores: 8.201553210^{-4}, 8.12712610^{-4}

  • Number of matched Beta training samples: 29814
  • Number of matched Beta validation samples: 20922

FS 3

  • caliper = 3.00
  • calcloset = TRUE
  • ratio = 1
  • replace = TRUE
  • 16x oversampling

Resultant scores: 0.0022122, 0.0022092

  • Number of matched Beta training samples: 3259
  • Number of matched Beta validation samples: 2199

FS 5

  • caliper = 0
  • calcloset = TRUE
  • ratio = 1
  • replace = FALSE
  • 8x oversampling

Resultant scores: 0.0015801, 0.0015768

  • Number of matched Beta training samples: 6208
  • Number of matched Beta validation samples: 4226

Nearest-Neighbors Logit, Linear

Hyperparameters: hyperparameter_tuning_nn_logit_linear.Rmd

FS 1

  • caliper = 0.4
  • calcloset = TRUE
  • ratio = 3
  • replace = FALSE
  • 4x oversampling
  • Interactions across covariates

Resultant scores: 7.762891510^{-4}, 7.683315510^{-4}

  • Number of matched Beta training samples: 44721
  • Number of matched Beta validation samples: 30644

FS 5

  • caliper = 0
  • calcloset = FALSE
  • ratio = 1
  • replace = FALSE
  • 8x oversampling

Resultant scores: 0.0013756, 0.001376

  • Number of matched Beta training samples: 7453
  • Number of matched Beta validation samples: 4825

Nearest-Neighbors GAM, Logit

Hyperparameters: hyperparameter_tuning_nn_gam_logit.Rmd

FS 1

  • caliper = 0.5
  • calcloset = FALSE
  • ratio = 3
  • replace = FALSE
  • 4x oversampling
  • Interactions across covariates

Resultant scores: 0.0011714, 0.0011762

  • Number of matched Beta training samples: 44721
  • Number of matched Beta validation samples: 30070

FS 5

  • caliper = 0
  • calcloset = FALSE
  • ratio = 1
  • replace = FALSE
  • 4x oversampling

Resultant scores: 0.002441, 0.0024395

  • Number of matched Beta training samples: 14907
  • Number of matched Beta validation samples: 9704

Nearest-Neighbors Probit, Linear

Hyperparameters: hyperparameter_tuning_nn_probit_linear.Rmd

FS 1

  • caliper = 0.4
  • calcloset = FALSE
  • ratio = 3
  • replace = FALSE
  • 4x oversampling
  • Interactions across covariates

Resultant scores: 7.430882310^{-4}, 7.367975710^{-4}

  • Number of matched Beta training samples: 44721
  • Number of matched Beta validation samples: 30607

FS 5

  • caliper = 0
  • calcloset = FALSE
  • ratio = 1
  • replace = FALSE
  • 8x oversampling

Resultant scores: 0.0013792, 0.001382

  • Number of matched Beta training samples: 7453
  • Number of matched Beta validation samples: 4958

Genetic Matching

Due to the very long training times of genetic matching, feature selection and hyperparameter tuning were not performed using bootstrap sampes. The models were previously trained using the full training sample. The resultant match beta subsets were serialized and uploaded to GCP.

Trained: feature_selection_genmatch_linear.Rmd

FS 1

Resultant scores: 0.001084, 0.001084

  • Number of matched Beta training samples: 25846
  • Number of matched Beta validation samples: 17684

Pop.Size 500

Resultant scores: 9.916087510^{-4}, 9.923251710^{-4}

  • Number of matched Beta training samples: 25930
  • Number of matched Beta validation samples: 17731

FS 3

Resultant scores: 0.0020662, 0.0020654

  • Number of matched Beta training samples: 22648
  • Number of matched Beta validation samples: 15929

FS 5

Resultant scores: 0.0020716, 0.0020748

  • Number of matched Beta training samples: 22469
  • Number of matched Beta validation samples: 15759

Results

Nearest-Neighbors Probit, Linear produces the optimal matching results. It yields the lowest scores, without dropping large sample of the Beta profiles. Unsurprisingly, matching directly, and only, on the performance metrics yields the best results. Interestingly, it appears that interactions across covariates greatly help. Fortunately, this model architecture is relatively cheap from a computational perspective.

Balancing on additional covariates (e.g, FS3 or FS5), outside of the performance metrics, yields sub-optimal results. Nearest-Neighbors Probit, Linear again yields the lowest score (0.0013792, 0.001382); however, this is at the expense of a greatly reduced Beta samples size (4958). Genetic matching is able to retain a much larger sample size (15929), at the expense of greater error (0.0020662, 0.0020654).

Conclusion: Optimal model framework

  • matching algorithm: Nearest-Neighbors Probit, Linear
  • features: Only performance metrics
  • hyperparameters:
    • caliper = 0.4
    • calcloset = FALSE
    • ratio = 3
    • replace = FALSE
    • 4x oversampling
    • Interactions across covariates

Push to GCP

The boostrap replicates for validation, oversampled traininig datasets, resultant quantile calculations, and matched datasets are saved to an R image. Then uploaded to the project GCP bucket.

Auto-refreshing stale OAuth token.
[1] "Previously trained results already exist: data/milestone2/validation_results_20191121.RData"
---
title: 'Milestone 2: Model Validation'
output:
  html_notebook:
    theme: cosmo
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
date: 'Last Updated: `r format(Sys.time(), "%B %d, %Y")`'
---

# tl;dr 
This notebook determines the [model framework](https://docs.google.com/document/d/1SfuanvmYmvmEFAdQ7Z5djDeLezdNB1TESqVmj93O8to/edit#heading=h.ex3kk7zd5a1y) utilized for this [PRD](https://docs.google.com/document/d/1Ygz6MkudYHZjnDnD9Z97kUyFrvV3KGWsjXyPjddhHq0/edit#heading=h.lvb9l8gw2nee). It utilizes the results from the hyperparameter tuning step, and trains the optimal model for each algorithm on the v67 dataset. The resultant predictions of these optimal models are compared to a v68 validation dataset. The framework used to generate the highest performant model (e.g., features, hyperparameters) will be used in online model training for the subsetting of Beta in production use.

```{r, echo=FALSE, warning=FALSE, message=FALSE}
source('../lib/supporting_funcs.R')
source('../lib/scoring.R')
library(MatchIt)
library(cowplot)
library(ggridges)
library(viridis)
```

```{r data_load, echo=FALSE}
file_name = 'df_train_validate_20191025.RData'
image_file_path = file.path('data', file_name)

# Pull from GCP if necessary
if (!file.exists(image_file_path)){
  Sys.setenv("GCS_DEFAULT_BUCKET" = "moz-fx-dev-subbeta",
           "GCS_AUTH_FILE" = "moz-fx-dev-cdowhyglund-subBeta-788f8f0d4627.json")
  library(googleCloudStorageR)
  gcs_get_object(file.path('data', 'milestone2', file_name), saveToDisk = image_file_path, overwrite = TRUE)
}

load(image_file_path)
```

```{r var_def, echo=FALSE}
df_rel_val <- df_validate_f %>%
  filter(label == 'release')

df_beta_train <- df_train_f %>% filter(is_release == FALSE)
df_beta_val <- df_validate_f %>% filter(is_release == FALSE)
df_rel_train <- df_train_f %>% filter(is_release == TRUE)
n_beta <- nrow(df_beta_val)
```

# Features
Feature sets: 

1. Only performance metrics.
2. Performance metrics and the highest found by Boruta with original dataset.
3. Performance metrics and the highest found by Boruta with equal labels dataset.
4. Performance metrics and all covariates.
5. Only the highest covariates found by Boruta (excluding performance).
6. Only the highest covariates found by Boruta (equal labels, excluding performance).
7. Utilize all covariates (excluding performance). 


```{r boruta_import, echo=FALSE, warning=FALSE, message=FALSE}
file_name = 'feature_selection_boruta_initial_20191023.RData'
image_file_path = file.path('data', file_name)

if (!file.exists(image_file_path)){
  Sys.setenv("GCS_DEFAULT_BUCKET" = "moz-fx-dev-subbeta",
           "GCS_AUTH_FILE" = "moz-fx-dev-cdowhyglund-subBeta-788f8f0d4627.json")
  library(googleCloudStorageR)
  gcs_get_object(file.path('data', 'milestone2', file_name), saveToDisk = image_file_path, overwrite = TRUE)
}
load(image_file_path)
```

```{r boruta_fs, echo=FALSE}
extract_boruta_fs <- function(boruta_res, num=5){
  features <- NULL
  for(metric in names(boruta_results)){
    features <- c(names(sort(apply(boruta_res[[metric]]$ImpHistory, 2, median), decreasing = TRUE)[1:num]), features)
  }
  return(sort(unique(features)))
}

features_top10 <- extract_boruta_fs(boruta_results, num=10)
features_top10_eq <- extract_boruta_fs(boruta_results_eq, num=10)

# filter out categorical
features_top10 <- df_train_f %>% 
  select(features_top10) %>% 
  select_if(is.numeric) %>% 
  names()
features_top10_eq <- df_train_f %>% 
  select(features_top10_eq) %>% 
  select_if(is.numeric) %>% 
  names()
```

```{r feature_sets, echo=FALSE}
perf_metrics <- names(get_m2_metric_map())

covs <- df_train_f %>%
  select(-perf_metrics) %>%
  select(-content_crashes) %>%
  select(-client_id) %>%
  select(-label) %>%
  select(-is_release) %>%
  select(-app_version) %>%
  select_if(is.numeric) %>% # Mahalanobis constraint
  names()

fs1 <- perf_metrics
fs2 <- c(names(fs1), features_top10)
fs3 <- c(names(fs1), features_top10_eq)
fs4 <- c(names(fs1), covs)
fs5 <- features_top10
fs6 <- features_top10_eq
fs7 <- covs
```

# Validation Bootstraps

30 bootstrap replicates of v68 Release will be utilized to validate the models. There are `r nrow(df_rel_val)` profiles utilized for this purpose. 

```{r bts_validate, echo=FALSE}
# create once
file_name = 'validation_bts_20191106.RData'
bts_file_path = file.path('data', file_name)

if (!file.exists(bts_file_path)){
  print('Creating validation bootstraps and saving')
  bts = list()
  for(i in 1:30){
    bts[[i]] <- df_rel_val %>% 
      sample_frac(size = 1, replace = TRUE) %>%
      pull(client_id) 
  }
  save(bts, file = bts_file_path)
} else {
  print('Loading previously created bootstraps from file')
  load(bts_file_path)
}
```

```{r scorer, echo=FALSE}
score_model <- function(bts, df_match, df_val, workers){
  if (missing(workers)) workers = detectCores()
  cl <- makePSOCKcluster(workers) 
  registerDoParallel(cl)
  final <- tryCatch({
    scores <- foreach(i=1:length(bts), 
                      .packages = c('dplyr', 'transport'), 
                      .export=c('calc_score', 'calc_cms', 'get_m2_metric_map')) %dopar% {
                        bt <- bts[[i]]
                        test <- df_val %>% 
                          right_join(data.frame(client_id = bt, stringsAsFactors=FALSE), by='client_id', 'right')
                        
                        df_scores <- test %>%
                          bind_rows(df_match)
                        
                        score <- calc_score(df_scores, get_m2_metric_map())
                        score
                        }
    scores <- unlist(scores)
    c(mean = mean(scores), median = median(scores))
  }, 
  error = function(cond){
    message(paste("Bootstrap validation failed: ", cond))
    return(NA)
  },
  finally = {
    stopCluster(cl)
  }
  )
  return(final)
}

build_quantile_df <- function(validation, matched, original){
  qqs <- list()
  for (perf_metric in perf_metrics){
    qq <- qqplot(validation[[perf_metric]], matched[[perf_metric]], plot.it = FALSE) %>% 
      bind_rows() %>%
      mutate(type = 'matched')
    qq_full <- qqplot(validation[[perf_metric]], original[[perf_metric]], plot.it = FALSE) %>% 
      bind_rows() %>%
      mutate(type = 'original') %>%
      bind_rows(qq) %>%
      mutate(metric = perf_metric)
    # qq_full$metric <- perf_metric
    qqs[[perf_metric]] <- qq_full
  }
  qq_df <- qqs %>% bind_rows() %>% rename(release = x, beta = y)
  return(qq_df)
}

plot_validation_results <- function(validation, matched, original, qq_df){
  if (missing(qq_df)) qq_df <- build_quantile_df(validation, matched, original)
  
  df <- matched %>%
      mutate(label = 'beta - matched') %>%
      bind_rows(validation) %>%
      bind_rows(original) %>%
      select(perf_metrics, label) # %>%
      # gather(key = 'metric', value = 'measurement', -label)
  
  plots <- list()
  for (pmet in perf_metrics){
    p_qq <- ggplot(qq_df %>% filter(metric == pmet), aes(x = release, y = beta)) +
      geom_point(aes(color = type, shape = type)) + 
      geom_abline(slope = 1, intercept = 0) + 
      theme_bw() + 
      theme(axis.text.x = element_text(angle = 45, hjust = 1),
            plot.title = element_text(size=10),
            legend.position = c(0.8, 0.2)) + 
      ggtitle(pmet) 
    
    p_ridge <- ggplot(df, aes(x=!!sym(pmet), y=label, fill=factor(..quantile..))) +
    stat_density_ridges(
      geom = "density_ridges_gradient", calc_ecdf = TRUE,
      quantiles = 10, quantile_lines = TRUE
    ) +
    scale_fill_viridis(discrete = TRUE, name = "Quartiles") + 
      theme_bw() +
      xlab(pmet) + 
      xlim(c(0, 10000)) + 
      guides(fill = FALSE)
    
    plots[[paste(pmet, 'qq', sep="_")]] <- p_qq
    plots[[paste(pmet, 'ridge', sep="_")]] <- p_ridge
  }
  
  print(plot_grid(plotlist = plots, ncol = 2))
}
```

# Oversampling

The entire v67 dataset will be utilized for training. However, most of the matching algorithms are more performant with higher ratios of Beta to Release. Therefore, various levels of oversampled datasets are created for training. There are `r n_beta` profiles utilized for this purpose.

```{r oversampling, echo=FALSE}
# create once

file_name = 'training_final_oversamples_20191106.RData'
oversamples_file_path = file.path('data', file_name)

if (!file.exists(oversamples_file_path)){
  print('Creating oversampling training sets and saving')
  oversample <- function(oversampling, df_beta, df_rel) {
    df_x <- df_rel %>%
      sample_n(size = round(n_beta / oversampling)) %>%
      rbind(df_beta)
    return(df_x)
    }
  
  oversamples <- c(1, 2, 4, 8, 16)
  dfs <- lapply(oversamples, oversample, df_beta = df_beta_train, df_rel = df_rel_train)
  names(dfs) <- as.character(oversamples)
  save(dfs, file = oversamples_file_path)
} else {
  print('Loading previously created oversampling training sets from file')
  load(oversamples_file_path)
}
```

# Model Training

Two matching methods were ultimately tested, as CEM and subclassing were producing extremely poor results. 

* nearest-neighbors
* genetic matching

For all models the following diagnostics are reported:

* mean, median score against the 30 validation replicates
* Number of matched Beta samples
* QQ and ridge plots of original and matched Beta samples

## Nearest-Neighbors, Malahanobis 

Hyperparameters: `hyperparameter_tuning_nn_malahanobis.Rmd`

Feature selection and hyperparameter tuning for the nearest-neighbors models were all trained using the `MatchIt` library.  These models are trained using the full, relevantly oversampled, training dataset.  

```{r trainer, echo=FALSE}
train_matchit <- function(train, model_covs, add_interactions, ...){
  # train model
  formula <- generate_formula(model_covs, label = 'is_release', add_interactions)
  model <- matchit(formula, train, ...)
  
  # extract beta subset
  df_matched <- get_matches(model, train) %>%
    select(-weights, -distance) %>%
    filter(label == 'beta')
  
  return(list(model = model, matched = df_matched))
}

extract_predictions <- function(matched, validation){
  prediction <- validation %>%
    filter(client_id %in% matched$client_id)
  
  return(prediction)
}
```

### FS 1

* `caliper` = 0.20
* `calcloset` = TRUE
* `ratio` = 2
* `replace` = FALSE
* 4x oversampling

```{r nn_mal_fs1, echo=FALSE}
# nn.mal.fs1 <- train_matchit(dfs[['4']], fs1, add_interactions = FALSE, replace = FALSE, 
#                         caliper = 0.25, calclosest = TRUE, ratio = 2, distance = "mahalanobis")
nn.mal.fs1.predictions <- extract_predictions(nn.mal.fs1$matched, df_beta_val)
nn.mal.fs1.qq <- build_quantile_df(df_rel_val, nn.mal.fs1.predictions, df_beta_val)
nn.mal.fs1.score <- score_model(bts, nn.mal.fs1.predictions, df_rel_val)
# nn.mal.fs1.score
```

Resultant scores: `r nn.mal.fs1.score`


* Number of matched Beta training samples: `r nrow(nn.mal.fs1$matched)`
* Number of matched Beta validation samples: `r nrow(nn.mal.fs1.predictions)`

```{r nn_mal_fs1_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.mal.fs1.predictions, df_beta_val, nn.mal.fs1.qq)
```

### FS 3 

* `caliper` = 3.00
* `calcloset` = TRUE
* `ratio` = 1
* `replace` = TRUE
* 16x oversampling

```{r nn_mal_fs3, echo=FALSE}
# nn.mal.fs3 <- train_matchit(dfs[['16']], fs3, add_interactions = FALSE, replace = TRUE, 
#                         caliper = 3.00, calclosest = TRUE, ratio = 1, distance = "mahalanobis")
nn.mal.fs3.predictions <- extract_predictions(nn.mal.fs3$matched, df_beta_val)
nn.mal.fs3.qq <- build_quantile_df(df_rel_val, nn.mal.fs3.predictions, df_beta_val)
nn.mal.fs3.score <- score_model(bts, nn.mal.fs3.predictions, df_rel_val)
# nn.mal.fs3.score
```

Resultant scores: `r nn.mal.fs3.score`


* Number of matched Beta training samples: `r nrow(nn.mal.fs3$matched)`
* Number of matched Beta validation samples: `r nrow(nn.mal.fs3.predictions)`

```{r nn_mal_fs3_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.mal.fs3.predictions, df_beta_val)
```



### FS 5

* `caliper` = 0
* `calcloset` = TRUE
* `ratio` = 1
* `replace` = FALSE
* 8x oversampling

```{r nn_mal_fs5, echo=FALSE}
# nn.mal.fs5 <- train_matchit(dfs[['8']], fs5, add_interactions = FALSE, replace = TRUE, 
#                         caliper = 0, calclosest = TRUE, ratio = 1, 
#                         distance = "mahalanobis")
nn.mal.fs5.predictions <- extract_predictions(nn.mal.fs5$matched, df_beta_val)
nn.mal.fs5.qq <- build_quantile_df(df_rel_val, nn.mal.fs5.predictions, df_beta_val)
nn.mal.fs5.score <- score_model(bts, nn.mal.fs5.predictions, df_rel_val)
# nn.mal.fs5.score
```

Resultant scores: `r nn.mal.fs5.score`


* Number of matched Beta training samples: `r nrow(nn.mal.fs5$matched)`
* Number of matched Beta validation samples: `r nrow(nn.mal.fs5.predictions)`

```{r nn_mal_fs5_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.mal.fs5.predictions, df_beta_val)
```

## Nearest-Neighbors Logit, Linear

Hyperparameters: `hyperparameter_tuning_nn_logit_linear.Rmd`

```{r feature_sets_nn, echo=FALSE}
# Reload to add additional categorical covariates

perf_metrics <- names(get_m2_metric_map())

covs <- df_train_f %>%
  select(-perf_metrics) %>%
  select(-content_crashes) %>%
  select(-client_id) %>%
  select(-label) %>%
  select(-is_release) %>%
  select(-app_version) %>%
  names()

fs1 <- perf_metrics
fs2 <- c(names(fs1), features_top10)
fs3 <- c(names(fs1), features_top10_eq)
fs4 <- c(names(fs1), covs)
fs5 <- features_top10
fs6 <- features_top10_eq
fs7 <- covs
```

### FS 1

* `caliper` = 0.4
* `calcloset` = TRUE
* `ratio` = 3
* `replace` = FALSE
* 4x oversampling
* Interactions across covariates

```{r nn_logit_fs1, warning=FALSE, error=FALSE, echo=FALSE}
# nn.linear.logit.fs1 <- train_matchit(dfs[['4']], fs1, add_interactions = TRUE, replace = FALSE, 
#                         caliper = 0.4, calclosest = TRUE, ratio = 3, distance = "linear.logit")
nn.linear.logit.fs1.predictions <- extract_predictions(nn.linear.logit.fs1$matched, df_beta_val)
nn.linear.logit.fs1.qq <- build_quantile_df(df_rel_val, nn.linear.logit.fs1.predictions, df_beta_val)
nn.linear.logit.fs1.score <- score_model(bts, nn.linear.logit.fs1.predictions, df_rel_val)
# nn.linear.logit.fs1.score
```

Resultant scores: `r nn.linear.logit.fs1.score`


* Number of matched Beta training samples: `r nrow(nn.linear.logit.fs1$matched)`
* Number of matched Beta validation samples: `r nrow(nn.linear.logit.fs1.predictions)`

```{r nn_logit_fs1_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.linear.logit.fs1.predictions, df_beta_val)
```

### FS 5

* `caliper` = 0
* `calcloset` = FALSE
* `ratio` = 1
* `replace` = FALSE
* 8x oversampling

```{r nn_logit_fs3, warning=FALSE, error=FALSE, echo=FALSE}
# nn.linear.logit.fs5 <- train_matchit(dfs[['8']], fs5, add_interactions = FALSE, replace = FALSE, 
#                         caliper = 0, calclosest = FALSE, ratio = 1, distance = "linear.logit")
nn.linear.logit.fs5.predictions <- extract_predictions(nn.linear.logit.fs5$matched, df_beta_val)
nn.linear.logit.fs5.qq <- build_quantile_df(df_rel_val, nn.linear.logit.fs5.predictions, df_beta_val)
nn.linear.logit.fs5.score <- score_model(bts, nn.linear.logit.fs5.predictions, df_rel_val)
# nn.linear.logit.fs5.score
```

Resultant scores: `r nn.linear.logit.fs5.score`


* Number of matched Beta training samples: `r nrow(nn.linear.logit.fs5$matched)`
* Number of matched Beta validation samples: `r nrow(nn.linear.logit.fs5.predictions)`

```{r nn_logit_fs5_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.linear.logit.fs5.predictions, df_beta_val)
```

## Nearest-Neighbors GAM, Logit

Hyperparameters: `hyperparameter_tuning_nn_gam_logit.Rmd`

### FS 1

* `caliper` = 0.5
* `calcloset` = FALSE
* `ratio` = 3
* `replace` = FALSE
* 4x oversampling
* Interactions across covariates

```{r nn_gam_fs1, warning=FALSE, error=FALSE, echo=FALSE, message=FALSE}
# nn.gam.fs1 <- train_matchit(dfs[['4']], fs1, add_interactions = TRUE, replace = FALSE, 
#                         caliper = 0.5, calclosest = TRUE, ratio = 3, distance = "GAMlogit")
nn.gam.fs1.predictions <- extract_predictions(nn.gam.fs1$matched, df_beta_val)
nn.gam.fs1.qq <- build_quantile_df(df_rel_val, nn.gam.fs1.predictions, df_beta_val)
nn.gam.fs1.score <- score_model(bts, nn.gam.fs1.predictions, df_rel_val)
# nn.gam.fs1.score
```

Resultant scores: `r nn.gam.fs1.score`


* Number of matched Beta training samples: `r nrow(nn.gam.fs1$matched)`
* Number of matched Beta validation samples: `r nrow(nn.gam.fs1.predictions)`

```{r nn_gam_fs1_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.gam.fs1.predictions, df_beta_val)
```


### FS 5

* `caliper` = 0
* `calcloset` = FALSE
* `ratio` = 1
* `replace` = FALSE
* 4x oversampling

```{r nn_gam_fs5, warning=FALSE, error=FALSE, echo=FALSE}
# nn.gam.fs5 <- train_matchit(dfs[['4']], fs5, add_interactions = FALSE, replace = FALSE, 
#                         caliper = 0, calclosest = FALSE, ratio = 1, distance = "GAMlogit")
nn.gam.fs5.predictions <- extract_predictions(nn.gam.fs5$matched, df_beta_val)
nn.gam.fs5.qq <- build_quantile_df(df_rel_val, nn.gam.fs5.predictions, df_beta_val)
nn.gam.fs5.score <- score_model(bts, nn.gam.fs5.predictions, df_rel_val)
# nn.gam.fs5.score
```

Resultant scores: `r nn.gam.fs5.score`


* Number of matched Beta training samples: `r nrow(nn.gam.fs5$matched)`
* Number of matched Beta validation samples: `r nrow(nn.gam.fs5.predictions)`

```{r nn_gam_fs5_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.gam.fs5.predictions, df_beta_val)
```

## Nearest-Neighbors Probit, Linear

Hyperparameters: `hyperparameter_tuning_nn_probit_linear.Rmd`

### FS 1

* `caliper` = 0.4
* `calcloset` = FALSE
* `ratio` = 3
* `replace` = FALSE
* 4x oversampling
* Interactions across covariates

```{r nn_probit_fs1, warning=FALSE, error=FALSE, echo=FALSE}
# nn.linear.probit.fs1 <- train_matchit(dfs[['4']], fs1, add_interactions = TRUE, replace = FALSE, 
#                         caliper = 0.4, calclosest = TRUE, ratio = 3, distance = "linear.probit")
nn.linear.probit.fs1.predictions <- extract_predictions(nn.linear.probit.fs1$matched, df_beta_val)
nn.linear.probit.fs1.qq <- build_quantile_df(df_rel_val, nn.linear.probit.fs1.predictions, df_beta_val)
nn.linear.probit.fs1.score <- score_model(bts, nn.linear.probit.fs1.predictions, df_rel_val)
# nn.linear.probit.fs1.score
```


Resultant scores: `r nn.linear.probit.fs1.score`


* Number of matched Beta training samples: `r nrow(nn.linear.probit.fs1$matched)`
* Number of matched Beta validation samples: `r nrow(nn.linear.probit.fs1.predictions)`

```{r nn_probit_fs1_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.linear.probit.fs1.predictions, df_beta_val)
```

### FS 5

* `caliper` = 0
* `calcloset` = FALSE
* `ratio` = 1
* `replace` = FALSE
* 8x oversampling

```{r nn_probit_fs5, warning=FALSE, error=FALSE, echo=FALSE}
# nn.linear.probit.fs5 <- train_matchit(dfs[['8']], fs5, add_interactions = FALSE, replace = FALSE, 
#                         caliper = 0, calclosest = FALSE, ratio = 1, distance = "linear.probit")
nn.linear.probit.fs5.predictions <- extract_predictions(nn.linear.probit.fs5$matched, df_beta_val)
nn.linear.probit.fs5.qq <- build_quantile_df(df_rel_val, nn.linear.probit.fs5.predictions, df_beta_val)
nn.linear.probit.fs5.score <- score_model(bts, nn.linear.probit.fs5.predictions, df_rel_val)
# nn.linear.probit.fs5.score
```

Resultant scores: `r nn.linear.probit.fs5.score`


* Number of matched Beta training samples: `r nrow(nn.linear.probit.fs5$matched)`
* Number of matched Beta validation samples: `r nrow(nn.linear.probit.fs5.predictions)`

```{r nn_probit_fs5_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, nn.linear.probit.fs5.predictions, df_beta_val)
```

## Genetic Matching

Due to the very long training times of genetic matching, feature selection and hyperparameter tuning were not performed using bootstrap sampes. The models were previously trained using the full training sample. The resultant match beta subsets were serialized and uploaded to GCP.

Trained: `feature_selection_genmatch_linear.Rmd`

```{r genmatch, echo=FALSE, error=FALSE, warning=FALSE}
file_name = 'feature_selection_genmatch_.RData'
image_file_path = file.path('data', file_name)

# Pull from GCP if necessary
if (!file.exists(image_file_path)){
  Sys.setenv("GCS_DEFAULT_BUCKET" = "moz-fx-dev-subbeta",
           "GCS_AUTH_FILE" = "moz-fx-dev-cdowhyglund-subBeta-788f8f0d4627.json")
  library(googleCloudStorageR)
  gcs_get_object(file.path('data', 'milestone2', file_name), saveToDisk = image_file_path, overwrite = TRUE)

}
load(image_file_path)
```

### FS 1

```{r genmatch_fs1, echo=FALSE}
genmatch.fs1.matched <- df_train_gen[fs1_results$matches$index.control,]
genmatch.fs1.predictions <- extract_predictions(genmatch.fs1.matched, df_beta_val)
genmatch.fs1.qq <- build_quantile_df(df_rel_val, genmatch.fs1.predictions, df_beta_val)
genmatch.fs1.score <- score_model(bts, genmatch.fs1.predictions, df_rel_val)
# genmatch.fs1.score
```

Resultant scores: `r genmatch.fs1.score`


* Number of matched Beta training samples: `r length(unique(genmatch.fs1.matched$client_id))`
* Number of matched Beta validation samples: `r nrow(genmatch.fs1.predictions)`

```{r genmatch_fs1_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, genmatch.fs1.predictions, df_beta_val)
```

#### Pop.Size 500

```{r genmatch_fs1_500, echo=FALSE}
genmatch.fs1.500.matched <- df_train_gen[fs1_500_results$matches$index.control,]
genmatch.fs1.500.predictions <- extract_predictions(genmatch.fs1.500.matched, df_beta_val)
genmatch.fs1.500.qq <- build_quantile_df(df_rel_val, genmatch.fs1.500.predictions, df_beta_val)
genmatch.fs1.500.score <- score_model(bts, genmatch.fs1.500.predictions, df_rel_val)
# genmatch.fs1.500.score
```

Resultant scores: `r genmatch.fs1.500.score`


* Number of matched Beta training samples: `r length(unique(genmatch.fs1.500.matched$client_id))`
* Number of matched Beta validation samples: `r nrow(genmatch.fs1.500.predictions)`

```{r genmatch_fs1.500_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, genmatch.fs1.500.predictions, df_beta_val)
```


### FS 3

```{r genmatch_fs3, echo=FALSE}
genmatch.fs3.matched <- df_train_gen[fs3_results$matches$index.control,]
genmatch.fs3.predictions <- extract_predictions(genmatch.fs3.matched, df_beta_val)
genmatch.fs3.qq <- build_quantile_df(df_rel_val, genmatch.fs3.predictions, df_beta_val)
genmatch.fs3.score <- score_model(bts, genmatch.fs3.predictions, df_rel_val)
# genmatch.fs3.score
```

Resultant scores: `r genmatch.fs3.score`


* Number of matched Beta training samples: `r length(unique(genmatch.fs3.matched$client_id))`
* Number of matched Beta validation samples: `r nrow(genmatch.fs3.predictions)`

```{r genmatch_fs3_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, genmatch.fs3.predictions, df_beta_val)
```

### FS 5

```{r genmatch_fs5, echo=FALSE}
genmatch.fs5.matched <- df_train_gen[fs5_results$matches$index.control,]
genmatch.fs5.predictions <- extract_predictions(genmatch.fs5.matched, df_beta_val)
genmatch.fs5.qq <- build_quantile_df(df_rel_val, genmatch.fs5.predictions, df_beta_val)
genmatch.fs5.score <- score_model(bts, genmatch.fs5.predictions, df_rel_val)
#genmatch.fs5.score
```

Resultant scores: `r genmatch.fs5.score`


* Number of matched Beta training samples: `r length(unique(genmatch.fs5.matched$client_id))`
* Number of matched Beta validation samples: `r nrow(genmatch.fs5.predictions)`

```{r genmatch_fs5_val_plt, fig.width=15,fig.height=25, warning=FALSE, message=FALSE, echo=FALSE}
plot_validation_results(df_rel_val, genmatch.fs5.predictions, df_beta_val)
```

# Results

Nearest-Neighbors Probit, Linear produces the optimal matching results. It yields the lowest scores, without dropping large sample of the Beta profiles. Unsurprisingly, matching directly, and only, on the performance metrics yields the best results. Interestingly, it appears that interactions across covariates greatly help. Fortunately, this model architecture is relatively cheap from a computational perspective. 

Balancing on additional covariates (e.g, FS3 or FS5), outside of the performance metrics, yields sub-optimal results. Nearest-Neighbors Probit, Linear again yields the lowest score (`r nn.linear.probit.fs5.score`); however, this is at the expense of a greatly reduced Beta samples size (`r nrow(nn.linear.probit.fs5.predictions)`). Genetic matching is able to retain a much larger sample size (`r nrow(genmatch.fs3.predictions)`), at the expense of greater error (`r genmatch.fs3.score`). 

**Conclusion**: Optimal model framework

* matching algorithm: Nearest-Neighbors Probit, Linear
* features: Only performance metrics
* hyperparameters: 
    * `caliper` = 0.4
    * `calcloset` = FALSE
    * `ratio` = 3
    * `replace` = FALSE
    * 4x oversampling
    * Interactions across covariates


# Push to GCP

The boostrap replicates for validation, oversampled traininig datasets, resultant quantile calculations, and matched datasets are saved to an R image. Then uploaded to the project GCP bucket.

```{r serialize_gcp, echo=FALSE}
results_file_name = 'validation_results_20191121.RData'
results_file_path = file.path('data', results_file_name)

objects <- c(
  ls(pattern = 'genmatch'),
  ls(pattern = 'nn'),
  ls(pattern = '^bts$'),
  ls(pattern = '^dfs$')
)

save(list = objects, file = results_file_path)
```


```{r push_to_gcp, echo=FALSE}
gcs_file_path <- file.path('data', 'milestone2', results_file_name)

Sys.setenv("GCS_DEFAULT_BUCKET" = "moz-fx-dev-subbeta",
           "GCS_AUTH_FILE" = "moz-fx-dev-cdowhyglund-subBeta-788f8f0d4627.json")

library(googleCloudStorageR)

proj_files = gcs_list_objects()

if (gcs_file_path %in% proj_files$name) {
  print(paste('Previously trained results already exist:', gcs_file_path))
} else {
  print(paste('Uploading validation results to GCP:', gcs_file_path))
  upload_try <- gcs_upload(results_file_path, name = gcs_file_path)
  upload_try
}
```



